<p>Polygeneration plants are proposed as an attractive solution to today’s challenging economic and political climate, whereby fossil fuels (e.g.: coal, natural gas) can be co-processed to obtain multiple products, such as electricity, gasoline and diesel. To this end, this thesis investigates the feasibility of the operation and control of a novel cooling system which incorporates steam methane reformer (SMR) tubes into a gasifier radiant syngas cooler (RSC). This approach capitalizes on available exergy by producing valuable H₂-rich synthesis gas (syngas) for liquid fuel production. As the device is still in the conceptual phase, a detailed multi-scale, two-dimensional, heterogeneous model has been developed in prior work to accurately predict the unit’s operation.</p> <p>A base case design was developed for both counter-current and co-current flow configurations, wherein a PI control structure designed to achieve performance objectives. Key trade-offs were found between the configurations: the counter-current design was more robust and effective in rejecting moderate and severe disturbances, while providing greater cooling duty and natural gas throughput, but at the expense of dangerously high tube wall temperatures, which can greatly reduce tube lifetime. The co-current design operates in a safer temperature range and satisfactorily rejects moderate disturbances, but requires feedforward control to handle extreme gasifier upsets.</p> <p>An offset-free linear model predictive controller (MPC) was developed for the co-current system to address process interactions. The MPC model was identified from ‘data’ derived from the rigorous plant model, with a Luenberger observer used to estimate and eliminate the plant-model mismatch. MPC offered superior set point tracking relative to discrete-PI control, especially in cases where discrete-PI destabilized the system. Using the co-current design, the flexibility of the device to adjust natural gas throughput based on variations in downstream syngas demand was demonstrated.</p> / Master of Applied Science (MASc)

<p>Environmental concerns about greenhouse gas emissions and energy security are the main drivers for the production of alternative fuels from bio-based feedstock. Dimethyl ether has attracted interest of many researches and is touted as “A fuel for the 21^st century” due to its versatility. However, the production of DME from biomass is dependent on the overall economics of its production.</p> <p>This thesis considers the application of semicontinuous distillation to improve the economics of the separation section in a biomass-to-DME facility. Semicontinuous distillation systems operate in a forced cycle to effect multiple separations using a single distillation column integrated with a middle vessel. The control system plays an integral role in the driving the forced cycle behaviour of the process in which no steady state exists.</p> <p>The separation section consists of a series of flash drums followed by a distillation train consisting of three (3) columns. In the first phase of this work, a semicontinuous system was developed to achieve the separation of the second and third distillation columns in the separation section. Rigorous models were used to simulate the semicontinuous system in which several control configurations were evaluated. The final control structure based on classic PI control was shown to achieve the specification objectives of the system and handle disturbances while avoiding weeping and flooding conditions. Optimization followed by an economic analysis showed that the semicontinuous system was economically preferable to the traditional continuous process for a range of DME production rates.</p> <p>Next, a semicontinuous system was developed to achieve the separation of the first and second distillation columns in the separation section. In this phase the application of semicontinuous distillation was extended to partial condenser configurations and the separation of biphasic mixtures. The control structure developed was effective in handling disturbance, attaining specification objectives while remaining with operational limits. An economic analysis, however, showed the traditional continuous configuration to be more economical for all DME production rates. Findings show that the operating cost is highly depending on the middle vessel purity so while uneconomical for this process it could result in favourable economics for less stringent purity specifications.</p> / Master of Applied Science (MASc)

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 196-209).

Thesis (Ph.D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 196-209) Also available in print.

<p>The industrial application of production planning and process scheduling optimization is addressed in this thesis. The first part of the thesis addresses the research into process scheduling application. Several scheduling models are developed based on both discrete and continuous time modelling frameworks. Extensions to both frameworks are presented to address unique production policies and maintenance activities. The potential benefits of schedule optimization is determined through several comparative industrial case studies. The weekly production schedules of the actual plant are compared against the schedules generated by optimization. The historical plant performance is ascertained and areas where efficiency gains are possible are highlighted. In addition, the scheduling model is used to investigate potential changes to production policies.</p> <p>The second part of the thesis addresses the research conducted in production planning application. The main goal of production planning is the efficient generation of a plan that specifies production targets for products over a medium term horizon. Direct application of previously proposed planning models fails to model several unique and key processing features of the production facility. A production planning model is presented that relaxes the detailed scheduling model structure and exploits the use of traveling salesman type constraints to accurately model sequence dependent changeovers. Two case studies are presented to investigate the benefits of optimization in production planning. The first case study investigates the lowest cost planning solution over a three month planning horizon. The second case study investigates the effects of a key production parameter on the optimality of solution. The results highlight the potential benefit of optimization application in increasing plant processing efficiency and reducing unnecessary production downtime.</p> <p>Finally, a modelling framework is presented that allows for the combined scheduling of production and maintenance. The framework allows for maintenance with various timing requirements and extends the capabilities of current frameworks.</p> / Master of Applied Science (MASc)

<p>Systematic strategies for optimal plant operation under partial shutdowns were developed. Partial shutdowns are circumscribed process unit shutdowns that permit the rest of the plant to continue operating to some degree. These strategies manipulate the degrees-of-freedom in a plant---during and after a shutdown---to restore plant production in a cost-optimal fashion, while meeting safety and operational constraints. This is accomplished through the adjustments of production rates, recycles and buffer levels.</p> <p>Our multi-tiered dynamic optimization approach allows for the prioritization of multiple objectives and the specification of trade-offs between these objectives. The solution of the optimization problem informs the formulation of inventory management policies. A Model-Predictive-Control (MPC) based partial shutdown algorithm implements these policies under feedback.</p> <p>Parsimonious discrete modeling formulations were presented for handling model discontinuities such as shutdown thresholds, induced shutdowns and minimum shutdown durations. The problem of minimizing restoration time was considered.</p> <p>We investigated the use of state/parameter estimation algorithms to moderate the effects of plant-model mismatch. The algorithms are based on novel configurations of the constrained Unscented Kalman Filter (UKF). Constraints on the estimates are enforced through a simple projection method. A dynamic feasibility tier ensures that terminal constraints and parameters are feasible for the prediction horizon in the control optimization problem.</p> <p>A modeling system (MLDO) was created for prototyping dynamic optimization models. It transforms a mathematical description of a model into code in various computer languages for the purposes of optimization, simulation, visualization and analysis of dynamic optimization problems. Facilities for problem reformulation and transformations are included.</p> / Doctor of Philosophy (PhD)

Thesis (Ph. D.)--Ohio University, June, 1995. / Title from PDF t.p.

<p>Current gasoline blend planning practice is to optimize blend plans via discrete-time multi-period NLP or MINLP models and schedule blends via interactive simulation. Solutions of multi-period models using discrete-time representation typically have different blend recipes for each time period. In this work, the concept of an inventory pinch point is introduced and used it to construct a new decomposition of the multi-period MINLP problems: at the top level nonlinear blending problems for periods delimited by the inventory pinch points are solved to optimize multi-grade blend recipes; at the lower level a fine grid multi-period MILP model that uses optimal recipes from the top level is solved in order to determine how much to blend of each product in each fine grid period, subject to minimum threshold blend size. If MILP is infeasible, corresponding period between the pinch points is subdivided and recipes are re-optimized.</p> <p>Two algorithms at the top level are examined: a) multi-period nonlinear model (MPIP) and b) single-period non-linear model (SPIP). Case studies show that the MPIP algorithm produces solutions that have the same optimal value of the objective function as corresponding MINLP model, while the SPIP algorithm computes solutions that are most often within 0.01% of the solutions by MINLP. Both algorithms require substantially less computational effort than the corresponding MINLP model. Reduced number of blend recipes makes it easier for blend scheduler to create a schedule by interactive simulation.</p> / Master of Applied Science (MASc)

<p>The electric arc furnace (EAF) is a highly energy intensive process used to convert scrap metal into molten steel. The aim of this research is to develop a dynamic model of an industrial EAF process, and investigate its application for optimal EAF operation. This work has three main contributions; the first contribution is developing a model largely based on MacRosty and Swartz (2005) to meet the operation of a new industrial partner (ArcelorMittal Contrecoeur Ouest, Quebec, Canada). The second contribution is carrying out sensitivity analyses to investigate the effect of the scrap components on the EAF process. Finally, the third contribution includes the development of a constrained multi-rate extended Kalman filter (EKF) to infer the states of the system from the measurements provided by the plant.</p> <p>A multi-zone model is developed and discussed in detail. Heat and mass transfer relationships are considered. Chemical equilibrium is assumed in two of the zones and calculated through the minimization of the Gibbs free energy. The most sensitive parameters are identified and estimated using plant measurements. The model is then validated against plant data and has shown a reasonable level of accuracy.</p> <p>Local differential sensitivity analysis is performed to investigate the effect of scrap components on the EAF operation. Iron was found to have the greatest effect amongst the components present. Then, the optimal operation of the furnace is determined through economic optimization. In this case, the trade-off between electrical and chemical energy is determined in order to maximize the profit. Different scenarios are considered that include price variation in electricity, methane and oxygen.</p> <p>A constrained multi-rate EKF is implemented in order to estimate the states of the system using plant measurements. The EKF showed high performance in tracking the true states of the process, even in the presence of a parametric plant-model mismatch.</p> / Master of Applied Science (MASc)

<p>This thesis considers the problems of modelling and control of batch processes, a class of finite duration chemical processes characterized by their absence of equilibrium conditions and nonlinear, time-varying dynamics over a wide range of operating conditions. In contrast to continuous processes, the control objective in batch processes is to achieve a non-equilibrium desired end-point or product quality by the batch termination time. However, the distinguishing features of batch processes complicate their control problem and call for dedicated modelling and control tools. In the initial phase of this research, a predictive controller based on the novel concept of reverse-time reachability regions (RTRRs) is developed. Defined as the set of states from where the process can be steered inside a desired end-point neighbourhood by batch termination subject to input constraints and model uncertainties, an algorithm is developed to characterize these sets at each sampling instance offline; these characterizations subsequently play an integral role in the control design. A key feature of the resultant controller is that it requires the online computation of only the immediate control action while guaranteeing reachability to the desired end-point neighbourhood, rendering the control problem efficiently solvable even when using the nonlinear process model. Moreover, the use of RTRRs and one-step ahead type control policy embeds important fault-tolerant characteristics into the controller. Next, we address the problem of the unavailability of reliable and computationally manageable first-principles-based process models by developing a new data-based modelling approach. In this approach, local linear models (identified via latent variable regression techniques) are combined with weights (arising from fuzzy c-means clustering) to describe global nonlinear process dynamics. Nonlinearities are captured through the appropriate combination of the different models while the linearity of the individual models prevents against a computationally expensive predictive controller. This modelling approach is also generalized to account for time-varying dynamics by incorporating online learning ability into the model, making it adaptive. This is accomplished by developing a probabilistic recursive least squares (PRLS) algorithm for updating a subset of the model parameters. The data-based modelling approach is first used to generate data-based reverse-time reachability regions (RTRRs), which are subsequently incorporated in a new predictive controller. Next, the modelling approach is applied on a complex nylon-6,6 batch polymerization process in order to design a trajectory tracking predictive controller for the key process outputs. Through simulations, the modelling approach is shown to capture the major process nonlinearities and closed-loop results demonstrate the advantages of the proposed controller over existing options. Through further simulation studies, model adaptation (via the PRLS algorithm) is shown to be crucial for achieving acceptable control performance when encountering large disturbances in the initial conditions. Finally, we consider the problem of direct quality control even when there are limited quality-related measurements available from the process; this situation typically calls for indirectly pursuing the control objective through trajectory tracking control. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behaviour until batch termination. This "missing data" problem is handled by integrating the previously developed data-based modelling approach with the inferential model in a predictive control framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via simulations of the nylon-6,6 batch polymerization process with a different control objective than considered previously.</p> / Doctor of Philosophy (PhD)